# Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts

arXiv: https://arxiv.org/abs/2211.14459 · HTML: https://mlsanity.com/paper/2211.14459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Word Level Language Identification in English Telugu Code Mixed Data](https://mlsanity.com/paper/2010.04482/) (2020)
- [Language Identification of Hindi-English tweets using code-mixed BERT](https://mlsanity.com/paper/2107.01202/) (2021)
- [CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts](https://mlsanity.com/paper/2211.09847/) (2022)

## Built on by (most cited first, 1 of 1)

- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)

## Abstract (arXiv)

Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesting problem of study in recent years due to the advancement and influences of social media in communication. This paper presents the Instituto Polit'ecnico Nacional, Centro de Investigaci'on en Computaci'on (CIC) team's system description paper for the CoLI-Kanglish shared task at ICON2022. In this paper, we propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.